Skip to main content Skip to complementary content

tElasticSearchConfiguration properties for Apache Spark Batch

These properties are used to configure tElasticSearchConfiguration running in the Spark Batch Job framework.

The Spark Batch tElasticSearchConfiguration component belongs to the ElasticSearch family.

The component in this framework is available in all Talend products with Big Data and Talend Data Fabric.

Basic settings

Nodes

Enter the location of the cluster hosting the Elasticsearch system to be used.

Transport addresses and Cluster name

Enter empty double quotation marks ("") in these fields.

Use SSL/TLS

Select this check box to enable the SSL or TLS encrypted connection.

Then you need to use the tSetKeystore component in the same Job to specify the encryption information.

User authentication

If the ElasticSearch system to be used requires authentication information, select this check box and enter the credentials.

Configuration

Add the parameters accepted by Elasticsearch to perform more customized actions.

For example, enter es.mapping.id in the Key column and true in the Value column to make the document field/property name contain the document id. Note that you must put double quotation marks around the entered information.

For a list of the parameters you can use, see https://www.elastic.co/guide/en/elasticsearch/hadoop/master/configuration.html.

Usage

Usage rule

This component is used with no need to be connected to other components.

Drop tElasticSearchConfiguration along with the ElasticSearch-related subJob to be run in the same Job so that the configuration is used by the whole Job at runtime.

  • Note that the Talend components for Spark Jobs support the Elasticsearch versions up to 6.4.2.

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs.

Spark Connection

In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files:
  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.

    • When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.

    • When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
    • When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.

    • When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

    If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis.

Did this page help you?

If you find any issues with this page or its content – a typo, a missing step, or a technical error – please let us know!

Leave your feedback here